device tensor fix
This commit is contained in:
Dobromir Popov
153
test_device_training_fix.py
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153
test_device_training_fix.py
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#!/usr/bin/env python3
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"""
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Test script to verify device handling and training sample population fixes
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"""
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import logging
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import asyncio
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import torch
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from datetime import datetime
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# Configure logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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def test_device_handling():
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"""Test that device handling is working correctly"""
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try:
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logger.info("Testing device handling...")
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# Test 1: Check CUDA availability
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cuda_available = torch.cuda.is_available()
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device = torch.device("cuda" if cuda_available else "cpu")
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logger.info(f"CUDA available: {cuda_available}")
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logger.info(f"Using device: {device}")
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# Test 2: Initialize CNN adapter
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from core.enhanced_cnn_adapter import EnhancedCNNAdapter
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logger.info("Initializing CNN adapter...")
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cnn_adapter = EnhancedCNNAdapter(checkpoint_dir="models/enhanced_cnn")
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logger.info(f"CNN adapter device: {cnn_adapter.device}")
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logger.info(f"CNN model device: {cnn_adapter.model.device}")
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# Test 3: Create test data
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from core.data_models import BaseDataInput
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logger.info("Creating test BaseDataInput...")
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base_data = BaseDataInput(
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symbol="ETH/USDT",
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timestamp=datetime.now(),
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ohlcv_1s=[],
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ohlcv_1m=[],
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ohlcv_1h=[],
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ohlcv_1d=[],
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btc_ohlcv_1s=[],
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cob_data=None,
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technical_indicators={},
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last_predictions={}
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)
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# Test 4: Make prediction (this should not cause device mismatch)
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logger.info("Making prediction...")
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prediction = cnn_adapter.predict(base_data)
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logger.info(f"Prediction successful: {prediction.predictions['action']}")
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logger.info(f"Confidence: {prediction.confidence:.4f}")
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# Test 5: Add training samples
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logger.info("Adding training samples...")
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cnn_adapter.add_training_sample(base_data, "BUY", 0.1)
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cnn_adapter.add_training_sample(base_data, "SELL", -0.05)
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cnn_adapter.add_training_sample(base_data, "HOLD", 0.02)
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logger.info(f"Training samples added: {len(cnn_adapter.training_data)}")
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# Test 6: Try training if we have enough samples
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if len(cnn_adapter.training_data) >= 2:
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logger.info("Attempting training...")
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training_results = cnn_adapter.train(epochs=1)
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logger.info(f"Training results: {training_results}")
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else:
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logger.info("Not enough samples for training")
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logger.info("✅ Device handling test passed!")
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return True
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except Exception as e:
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logger.error(f"❌ Device handling test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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async def test_orchestrator_training():
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"""Test that orchestrator properly adds training samples"""
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try:
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logger.info("Testing orchestrator training integration...")
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# Test 1: Initialize orchestrator
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from core.orchestrator import TradingOrchestrator
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from core.standardized_data_provider import StandardizedDataProvider
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logger.info("Initializing data provider...")
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data_provider = StandardizedDataProvider()
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logger.info("Initializing orchestrator...")
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orchestrator = TradingOrchestrator(data_provider=data_provider)
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# Test 2: Check if CNN adapter is available
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if hasattr(orchestrator, 'cnn_adapter') and orchestrator.cnn_adapter:
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logger.info(f"✅ CNN adapter available in orchestrator")
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logger.info(f"Initial training samples: {len(orchestrator.cnn_adapter.training_data)}")
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else:
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logger.warning("⚠️ CNN adapter not available in orchestrator")
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return False
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# Test 3: Make a trading decision (this should add training samples)
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logger.info("Making trading decision...")
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decision = await orchestrator.make_trading_decision("ETH/USDT")
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if decision:
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logger.info(f"Decision: {decision.action} (confidence: {decision.confidence:.4f})")
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logger.info(f"Training samples after decision: {len(orchestrator.cnn_adapter.training_data)}")
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else:
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logger.warning("No decision made")
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# Test 4: Check inference history
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logger.info(f"Inference history keys: {list(orchestrator.inference_history.keys())}")
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for model_name, history in orchestrator.inference_history.items():
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logger.info(f" {model_name}: {len(history)} records")
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logger.info("✅ Orchestrator training test passed!")
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return True
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except Exception as e:
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logger.error(f"❌ Orchestrator training test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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async def main():
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"""Run all tests"""
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logger.info("Starting device and training fix tests...")
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# Test 1: Device handling
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test1_passed = test_device_handling()
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# Test 2: Orchestrator training
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test2_passed = await test_orchestrator_training()
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# Summary
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logger.info("\n" + "="*50)
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logger.info("TEST SUMMARY:")
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logger.info(f"Device handling: {'✅ PASSED' if test1_passed else '❌ FAILED'}")
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logger.info(f"Orchestrator training: {'✅ PASSED' if test2_passed else '❌ FAILED'}")
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if test1_passed and test2_passed:
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logger.info("🎉 All tests passed! Device and training issues should be fixed.")
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else:
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logger.error("❌ Some tests failed. Please check the logs above.")
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if __name__ == "__main__":
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asyncio.run(main())
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